---
id: policies
sidebar_label: Policies
title: Policies
abstract: Your assistant uses policies to decide which action to take at each step in a conversation. There are machine-learning and rule-based policies that your assistant can use in tandem.
---
You can customize the policies your assistant uses by specifying the `policies`
key in your project's `config.yml`.
There are different policies to choose from, and you can include
multiple policies in a single configuration. Here's an example of
what a list of policies might look like:

```yaml-rasa title="config.yml"
language:  # your language
pipeline:
  # - <pipeline components>

policies:
  - name: MemoizationPolicy
  - name: TEDPolicy
    max_history: 5
    epochs: 200
  - name: RulePolicy
```
:::tip Starting from scratch?

If you don't know which policies to choose, leave out the `policies` key from your `config.yml` completely.
If you do, the [Suggested Config](.//model-configuration.mdx#suggested-config)
feature will provide default policies for you.

:::

## Action Selection

At every turn, each policy defined in your configuration will
predict a next action with a certain confidence level. For more information
about how each policy makes its decision, read into the policy's description below.
The policy that predicts with the highest confidence decides the assistant's next action.

:::note Maximum number of predictions
By default, your assistant can predict a maximum of 10 next actions
after each user message. To update this value,
you can set the environment variable `MAX_NUMBER_OF_PREDICTIONS`
to the desired number of maximum predictions.

:::

### Policy Priority

In the case that two policies predict with equal confidence (for example, the Memoization
and Rule Policies might both predict with confidence 1), the priority of the
policies is considered. Rasa Open Source policies have default priorities that are set to ensure the
expected outcome in the case of a tie. They look like this, where higher numbers have higher priority:

<!-- We want to have high priority policies first; it's not possible to use a Markdown ordered list for that. -->

* 6 - `RulePolicy`

* 3 - `MemoizationPolicy` or `AugmentedMemoizationPolicy`

* 1 - `TEDPolicy`


In general, it is not recommended to have more
than one policy per priority level in your configuration. If you have 2 policies with the same priority and they predict
with the same confidence, the resulting action will be chosen randomly.

If you create your own policy, use these priorities as a guide for figuring out the priority of your policy.
If your policy is a machine learning policy, it should most likely have priority 1, the same as the `TEDPolicy`.

:::warning overriding policy priorities
All policy priorities are configurable via the `priority` parameter in the policy's configuration,
but we **do not recommend** changing them outside of specific cases such as custom policies.
Doing so can lead to unexpected and undesired bot behavior.

:::


## Machine Learning Policies

### TED Policy

The Transformer Embedding Dialogue (TED) Policy is
a multi-task architecture for next action prediction and entity
recognition. The architecture several transformer encoders which are shared for both tasks.
A sequence of entity labels is predicted through a Conditional Random Field (CRF) tagging layer on top of the
user sequence transformer encoder output corresponding to the input sequence of tokens.
For the next action prediction the dialogue transformer encoder output and system action labels are embedded into a
single semantic vector space. We use the dot-product loss to maximize the similarity with the target label and
minimize similarities with negative samples.

If you want to learn more about the model, check out
[our paper](https://arxiv.org/abs/1910.00486) and on our
[youtube channel](https://www.youtube.com/watch?v=j90NvurJI4I&list=PL75e0qA87dlG-za8eLI6t0_Pbxafk-cxb&index=14&ab_channel=Rasa).
where we explain the model architecture in detail.

TED Policy architecture comprises the following steps:

1. Concatenate features for
   - user input (user intent and entities) or user text processed through a user sequence transformer encoder,
   - previous system actions or bot utterances processed through a bot sequence transformer encoder,
   - slots and active forms

   for each time step into an input vector to the embedding layer that precedes the 
   dialogue transformer.

2. Feed the embedding of the input vector into the dialogue transformer encoder.

3. Apply a dense layer to the output of the dialogue transformer to get embeddings of the dialogue for each time step.

4. Apply a dense layer to create embeddings for system actions for each time step.

5. Calculate the similarity between the dialogue embedding and embedded system actions.
   This step is based on the [StarSpace](https://arxiv.org/abs/1709.03856) idea.

6. Concatenate the token-level output of the user sequence transformer encoder
   with the output of the dialogue transformer encoder for each time step.

7. Apply CRF algorithm to predict contextual entities for each user text input.

**Configuration:**

You can pass configuration parameters to the `TEDPolicy` using the `config.yml` file.
If you want to fine-tune your model, start by modifying the following parameters:

* `epochs`:
  This parameter sets the number of times the algorithm will see the training data (default: `1`).
  One `epoch` is equals to one forward pass and one backward pass of all the training examples.
  Sometimes the model needs more epochs to properly learn.
  Sometimes more epochs don't influence the performance.
  The lower the number of epochs the faster the model is trained.
  Here is how the config would look like:

  ```yaml-rasa title="config.yml"
  policies:
  - name: TEDPolicy
    epochs: 200
  ```

* `max_history`:
  This parameter controls how much dialogue history the model looks at to decide which
  action to take next. Default `max_history` for this policy is `None`,
  which means that the complete dialogue history since session restart is taken into
  account. If you want to limit the model to only see a certain number of previous
  dialogue turns, you can set `max_history` to a finite value.
  Please note that you should pick `max_history` carefully, so that the model has enough
  previous dialogue turns to create a correct prediction.
  See [Featurizers](#featurizers) for more details.
  Here is how the config would look like:

  ```yaml-rasa title="config.yml"
  policies:
  - name: TEDPolicy
    max_history: 8
  ```

* `number_of_transformer_layers`:
  This parameter sets the number of sequence transformer encoder layers to use for
  sequential transformer encoders for user, action and action label texts and for
  dialogue transformer encoder.
  (defaults: `text: 1, action_text: 1, label_action_text: 1, dialogue: 1`).
  The number of sequence transformer encoder layers corresponds
  to the transformer blocks to use for the model.

* `transformer_size`:
  This parameter sets the number of units in the sequence transformer encoder layers to use for
  sequential transformer encoders for user, action and action label texts and for
  dialogue transformer encoder.
  (defaults: `text: 128, action_text: 128, label_action_text: 128, dialogue: 128`).
  The vectors coming out of the transformer encoders will have the given `transformer_size`.

* `weight_sparsity`:
  This parameter defines the fraction of kernel weights that are set to 0 for all feed forward layers
  in the model (default: `0.8`). The value should be a number between 0 and 1. If you set `weight_sparsity`
  to 0, no kernel weights will be set to 0, the layer acts as a standard feed forward layer. You should not
  set `weight_sparsity` to 1 as this would result in all kernel weights being 0, i.e. the model is not able
  to learn.

The above configuration parameters are the ones you should configure to fit your model to your data.
However, additional parameters exist that can be adapted.

<details><summary>More configurable parameters</summary>

```
+---------------------------------------+------------------------+--------------------------------------------------------------+
| Parameter                             | Default Value          | Description                                                  |
+=======================================+========================+==============================================================+
| hidden_layers_sizes                   | text: []               | Hidden layer sizes for layers before the embedding layers    |
|                                       | action_text: []        | for user messages and bot messages in previous actions       |
|                                       | label_action_text: []  | and labels. The number of hidden layers is                   |
|                                       |                        | equal to the length of the corresponding list.               |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| dense_dimension                       | text: 128              | Dense dimension for sparse features to use after they are    |
|                                       | action_text: 128       | converted into dense features.                               |
|                                       | label_action_text: 128 |                                                              |
|                                       | intent: 20             |                                                              |
|                                       | action_name: 20        |                                                              |
|                                       | label_action_name: 20  |                                                              |
|                                       | entities: 20           |                                                              |
|                                       | slots: 20              |                                                              |
|                                       | active_loop: 20        |                                                              |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| concat_dimension                      | text: 128              | Common dimension to which sequence and sentence features of  |
|                                       | action_text: 128       | different dimensions get converted before concatenation.     |
|                                       | label_action_text: 128 |                                                              |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| encoding_dimension                    | 50                     | Dimension size of embedding vectors                          |
|                                       |                        | before the dialogue transformer encoder.                     |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| transformer_size                      | text: 128              | Number of units in user text sequence transformer encoder.   |
|                                       | action_text: 128       | Number of units in bot text sequence transformer encoder.    |
|                                       | label_action_text: 128 | Number of units in bot text sequence transformer encoder.    |
|                                       | dialogue: 128          | Number of units in dialogue transformer encoder.             |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| number_of_transformer_layers          | text: 1                | Number of layers in user text sequence transformer encoder.  |
|                                       | action_text: 1         | Number of layers in bot text sequence transformer encoder.   |
|                                       | label_action_text: 1   | Number of layers in bot text sequence transformer encoder.   |
|                                       | dialogue: 1            | Number of layers in dialogue transformer encoder.            |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| number_of_attention_heads             | 4                      | Number of self-attention heads in transformers.              |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| use_key_relative_attention            | False                  | If 'True' use key relative embeddings in attention.          |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| use_value_relative_attention          | False                  | If 'True' use value relative embeddings in attention.        |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| max_relative_position                 | None                   | Maximum position for relative embeddings.                    |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| batch_size                            | [64, 256]              | Initial and final value for batch sizes.                     |
|                                       |                        | Batch size will be linearly increased for each epoch.        |
|                                       |                        | If constant `batch_size` is required, pass an int, e.g. `8`. |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| batch_strategy                        | "balanced"             | Strategy used when creating batches.                         |
|                                       |                        | Can be either 'sequence' or 'balanced'.                      |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| epochs                                | 1                      | Number of epochs to train.                                   |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| random_seed                           | None                   | Set random seed to any 'int' to get reproducible results.    |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| embedding_dimension                   | 20                     | Dimension size of dialogue & system action embedding vectors.|
+---------------------------------------+------------------------+--------------------------------------------------------------+
| number_of_negative_examples           | 20                     | The number of incorrect labels. The algorithm will minimize  |
|                                       |                        | their similarity to the user input during training.          |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| similarity_type                       | "auto"                 | Type of similarity measure to use, either 'auto' or 'cosine' |
|                                       |                        | or 'inner'.                                                  |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| loss_type                             | "softmax"              | The type of the loss function, either 'softmax' or 'margin'. |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| ranking_length                        | 10                     | Number of top actions to normalize scores for loss type      |
|                                       |                        | 'softmax'. Set to 0 to turn off normalization.               |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| maximum_positive_similarity           | 0.8                    | Indicates how similar the algorithm should try to make       |
|                                       |                        | embedding vectors for correct labels.                        |
|                                       |                        | Should be 0.0 < ... < 1.0 for 'cosine' similarity type.      |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| maximum_negative_similarity           | -0.2                   | Maximum negative similarity for incorrect labels.            |
|                                       |                        | Should be -1.0 < ... < 1.0 for 'cosine' similarity type.     |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| use_maximum_negative_similarity       | True                   | If 'True' the algorithm only minimizes maximum similarity    |
|                                       |                        | over incorrect intent labels, used only if 'loss_type' is    |
|                                       |                        | set to 'margin'.                                             |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| scale_loss                            | True                   | Scale loss inverse proportionally to confidence of correct   |
|                                       |                        | prediction.                                                  |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| regularization_constant               | 0.001                  | The scale of regularization.                                 |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| negative_margin_scale                 | 0.8                    | The scale of how important it is to minimize the maximum     |
|                                       |                        | similarity between embeddings of different labels.           |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| drop_rate_dialogue                    | 0.1                    | Dropout rate for embedding layers of dialogue features.      |
|                                       |                        | Value should be between 0 and 1.                             |
|                                       |                        | The higher the value the higher the regularization effect.   |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| drop_rate_label                       | 0.0                    | Dropout rate for embedding layers of label features.         |
|                                       |                        | Value should be between 0 and 1.                             |
|                                       |                        | The higher the value the higher the regularization effect.   |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| drop_rate_attention                   | 0.0                    | Dropout rate for attention. Value should be between 0 and 1. |
|                                       |                        | The higher the value the higher the regularization effect.   |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| weight_sparsity                       | 0.8                    | Sparsity of the weights in dense layers.                     |
|                                       |                        | Value should be between 0 and 1.                             |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| use_sparse_input_dropout              | True                   | If 'True' apply dropout to sparse input tensors.             |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| use_dense_input_dropout               | True                   | If 'True' apply dropout to sparse features after they are    |
|                                       |                        | converted into dense features.                               |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| evaluate_every_number_of_epochs       | 20                     | How often to calculate validation accuracy.                  |
|                                       |                        | Set to '-1' to evaluate just once at the end of training.    |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| evaluate_on_number_of_examples        | 0                      | How many examples to use for hold out validation set.        |
|                                       |                        | Large values may hurt performance, e.g. model accuracy.      |
|                                       |                        | Keep at 0 if your data set contains a lot of unique examples |
|                                       |                        | of dialogue turns.                                           |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| tensorboard_log_directory             | None                   | If you want to use tensorboard to visualize training         |
|                                       |                        | metrics, set this option to a valid output directory. You    |
|                                       |                        | can view the training metrics after training in tensorboard  |
|                                       |                        | via 'tensorboard --logdir <path-to-given-directory>'.        |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| tensorboard_log_level                 | "epoch"                | Define when training metrics for tensorboard should be       |
|                                       |                        | logged. Either after every epoch ('epoch') or for every      |
|                                       |                        | training step ('minibatch').                                 |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| checkpoint_model                      | False                  | Save the best performing model during training. Models are   |
|                                       |                        | stored to the location specified by `--out`. Only the one    |
|                                       |                        | best model will be saved.                                    |
|                                       |                        | Requires `evaluate_on_number_of_examples > 0` and            |
|                                       |                        | `evaluate_every_number_of_epochs > 0`                        |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| e2e_confidence_threshold              | 0.5                    | The threshold that ensures that end-to-end is picked only if |
|                                       |                        | the policy is confident enough.                              |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| featurizers                           | []                     | List of featurizer names (alias names). Only features        |
|                                       |                        | coming from the listed names are used. If list is empty      |
|                                       |                        | all available features are used.                             |
+---------------------------------------+------------------------+--------------------------------------------------------------+
| entity_recognition                    | True                   | If 'True' entity recognition is trained and entities are     |
|                                       |                        | extracted.                                                   |
+---------------------------------------+------------------------+--------------------------------------------------------------+
```

:::note
The parameter `maximum_negative_similarity` is set to a negative value to mimic the original
starspace algorithm in the case `maximum_negative_similarity = maximum_positive_similarity` and
`use_maximum_negative_similarity = False`. See [starspace paper](https://arxiv.org/abs/1709.03856)
for details.

:::

</details>

### Memoization Policy

The `MemoizationPolicy` remembers the stories from your
training data. It checks if the current conversation matches the stories in your
`stories.yml` file. If so, it will predict the next action from the matching
stories of your training data with a confidence of `1.0`. If no matching conversation
is found, the policy predicts `None` with confidence `0.0`.

When looking for a match in your training data, the policy will take the last
`max_history` number of turns of the conversation into account.
One “turn” includes the message sent by the user and any actions the
assistant performed before waiting for the next message.

You can configure the number of turns the `MemoizationPolicy` should use in your
configuration:
```yaml title="config.yml"
policies:
  - name: "MemoizationPolicy"
    max_history: 3
```


### Augmented Memoization Policy

The `AugmentedMemoizationPolicy` remembers examples from training
stories for up to `max_history` turns, just like the `MemoizationPolicy`.
Additionally, it has a forgetting mechanism that will forget a certain amount
of steps in the conversation history and try to find a match in your stories
with the reduced history. It predicts the next action with confidence `1.0`
if a match is found, otherwise it predicts `None` with confidence `0.0`.

:::note Slots and predictions
If you have dialogues where some slots that are set during
prediction time might not be set in training stories (e.g. in training
stories starting with a [reminder](./reaching-out-to-user.mdx#reminders), not all previous slots are set),
make sure to add the relevant stories without slots to your training
data as well.

:::


## Rule-based Policies

### Rule Policy

The `RulePolicy` is a policy that handles conversation parts that follow
a fixed behavior (e.g. business logic). It makes predictions based on
any `rules` you have in your training data. See the
[Rules documentation](./rules.mdx) for further information on how to define rules.

The `RulePolicy` has the following configuration options:

```yaml title="config.yml"
policies:
  - name: "RulePolicy"
    core_fallback_threshold: 0.3
    core_fallback_action_name: action_default_fallback
    enable_fallback_prediction: true
    restrict_rules: true
    check_for_contradictions: true
```

* `core_fallback_threshold` (default: `0.3`): Please see the
   [fallback documentation](fallback-handoff.mdx#handling-low-action-confidence) for
   further information.
* `core_fallback_action_name` (default: `action_default_fallback`): Please see the
   [fallback documentation](fallback-handoff.mdx#handling-low-action-confidence) for
   further information.
* `enable_fallback_prediction` (default: `true`): Please see the
   [fallback documentation](fallback-handoff.mdx#handling-low-action-confidence) for
   further information.
* `check_for_contradictions` (default: `true`):
   Before training, the RulePolicy will perform a check to make sure that
   slots and active loops set by actions are defined consistently for all rules.
   The following snippet contains an example of an incomplete rule:

   ```yaml-rasa
   rules:
   - rule: complete rule
     steps:
     - intent: search_venues
     - action: action_search_venues
     - slot_was_set:
       - venues: [{"name": "Big Arena", "reviews": 4.5}]

   - rule: incomplete rule
     steps:
     - intent: search_venues
     - action: action_search_venues
   ```

   In the second `incomplete rule`, `action_search_venues` should set
   the `venues` slot because it is set in `complete rule`, but this event is missing.
   There are several possible ways to fix this rule.

   In the case when `action_search_venues` can't find
   a venue and the `venues` slot should not be set,
   you should explicitly set the value of the slot to `null`.
   In the following story `RulePolicy` will predict `utter_venues_not_found`
   only if the slot `venues` is not set:

   ```yaml-rasa
   rules:
   - rule: fixes incomplete rule
     steps:
     - intent: search_venues
     - action: action_search_venues
     - slot_was_set:
       - venues: null
     - action: utter_venues_not_found
   ```

   If you want the slot setting to be handled by a different rule or story,
   you should add `wait_for_user_input: false` to the end of the rule snippet:

   ```yaml-rasa
   rules:
   - rule: incomplete rule
     steps:
     - intent: search_venues
     - action: action_search_venues
     wait_for_user_input: false
   ```

   After training, the RulePolicy will check that none of the rules or stories contradict
   each other. The following snippet is an example of two contradicting rules:

    ```yaml-rasa
    rules:
    - rule: Chitchat
      steps:
      - intent: chitchat
      - action: utter_chitchat

    - rule: Greet instead of chitchat
      steps:
      - intent: chitchat
      - action: utter_greet  # `utter_greet` contradicts `utter_chitchat` from the rule above
    ```
 * `restrict_rules` (default: `true`): Rules are restricted to one user turn, but
    there can be multiple bot events, including e.g. a form being filled and its subsequent submission.
    Changing this parameter to `false` may result in unexpected behavior.

  :::caution Overusing rules
    Overusing rules for purposes outside of the [recommended use cases](rules.mdx)
    will make it very hard to maintain your assistant as the complexity grows.

  :::

## Configuring Policies

### Max History

One important hyperparameter for Rasa Open Source policies is the `max_history`.
This controls how much dialogue history the model looks at to decide which
action to take next.

You can set the `max_history` by passing it to your policy
in the policy configuration in your `config.yml`.
The default value is `None`, which means that the complete dialogue history since session
restart is taken in the account.

```yaml-rasa title="config.yml" {3}
policies:
  - name: TEDPolicy
    max_history: 5
    epochs: 200
    batch_size: 50
    max_training_samples: 300
```

:::note
`RulePolicy` doesn't have max history parameter, it always consider the full length
of provided rules. Please see [Rules](./rules.mdx) for further information.
:::

As an example, let's say you have an `out_of_scope` intent which
describes off-topic user messages. If your bot sees this intent multiple
times in a row, you might want to tell the user what you can help them
with. So your story might look like this:

```yaml-rasa
stories:
  - story: utter help after 2 fallbacks
    steps:
    - intent: out_of_scope
    - action: utter_default
    - intent: out_of_scope
    - action: utter_default
    - intent: out_of_scope
    - action: utter_help_message
```

For your model to learn this pattern, the `max_history`
has to be at least 4.

If you increase your `max_history`, your model will become bigger and
training will take longer. If you have some information that should
affect the dialogue very far into the future, you should store it as a
slot. Slot information is always available for every featurizer.

### Data Augmentation

When you train a model, Rasa Open Source will create
longer stories by randomly combining
the ones in your stories files.
Take the stories below as an example:

```yaml-rasa
stories:
  - story: thank
    steps:
    - intent: thankyou
    - action: utter_youarewelcome
  - story: say goodbye
    steps:
    - intent: goodbye
    - action: utter_goodbye
```

You actually want to teach your policy to **ignore** the dialogue history
when it isn't relevant and to respond with the same action no matter
what happened before.

You can alter this behavior with the `--augmentation` flag,
which allows you to set the `augmentation_factor`.
The `augmentation_factor` determines how many augmented stories are
subsampled during training. The augmented stories are subsampled before training
since their number can quickly become very large, and you want to limit it.
The number of sampled stories is `augmentation_factor` x10.
By default augmentation is set to 20, resulting in a maximum of 200 augmented stories.

`--augmentation 0` disables all augmentation behavior.
The memoization based policies are not affected by augmentation
(independent of the `augmentation_factor`) and will automatically
ignore all augmented stories.

### Featurizers

In order to apply machine learning algorithms to conversational AI, you need
to build up vector representations of conversations.

Each story corresponds to a tracker which consists of the states of the
conversation just before each action was taken.

#### State Featurizers

Every event in a trackers history creates a new state (e.g. running a bot
action, receiving a user message, setting slots). Featurizing a single state
of the tracker has two steps:

1. **Tracker provides a bag of active features**:

    * features indicating intents and entities, if this is the first
     state in a turn, e.g. it's the first action we will take after
     parsing the user's message. (e.g.
     `[intent_restaurant_search, entity_cuisine]` )

    * features indicating which slots are currently defined, e.g.
     `slot_location` if the user previously mentioned the area
     they're searching for restaurants.

    * features indicating the results of any API calls stored in
     slots, e.g. `slot_matches`

    * features indicating what the last bot action or bot utterance was (e.g.
     `prev_action_listen`)

    * features indicating if any loop is active and which one

2. **Convert all the features into numeric vectors**:

    `SingleStateFeaturizer` uses the Rasa NLU pipeline to convert the intent and
    bot action names or bot utterances into numeric vectors.
    See the [NLU Model Configuration](./model-configuration.mdx) documentation
    for the details on how to configure Rasa NLU pipeline.

    Entities, slots and active loops are featurized as one-hot encodings
    to indicate their presence.

:::note
If the domain defines the possible `actions`,
`[ActionGreet, ActionGoodbye]`,
4 additional default actions are added:
`[ActionListen(), ActionRestart(),
ActionDefaultFallback(), ActionDeactivateForm()]`.
Therefore, label `0` indicates default action listen, label `1`
default restart, label `2` a greeting and `3` indicates goodbye.

:::

#### Tracker Featurizers

It's often useful to include a bit more history than just the current state
when predicting an action. The `TrackerFeaturizer` iterates over tracker
states and calls a `SingleStateFeaturizer` for each state to create numeric
input features for a policy.
The target labels correspond to bot actions or bot utterances
represented as index in a list of all possible actions.

There are two different tracker featurizers:

##### 1. Full Dialogue

`FullDialogueTrackerFeaturizer` creates a numerical representation of
stories to feed to a recurrent neural network where the whole dialogue
is fed to a network and the gradient is backpropagated from all time steps.
The smaller dialogues are padded with `0` for all features.

##### 2. Max History

`MaxHistoryTrackerFeaturizer` creates an array of previous tracker
states for each bot action or bot utterance, with the parameter
`max_history` defining how many states go into each row of input features.
If `max_history` is not specified, the algorithm takes
the whole length of a dialogue into account.
The smaller dialogues are padded with `0` for all features.
Deduplication is performed to filter out duplicated turns (bot actions
or bot utterances) in terms of their previous states.

For some algorithms a flat feature vector is needed, so input features
should be reshaped to `(num_unique_turns, max_history * num_input_features)`.

## Custom Policies

You can also write custom policies and reference them in your configuration. In the example below, the
last two lines show how to use a custom policy class and pass arguments to it.

```yaml-rasa {9-10}
policies:
  - name: "TEDPolicy"
    max_history: 5
    epochs: 200
  - name: "RulePolicy"
  - name: "path.to.your.policy.class"
    arg1: "..."
```

## Deprecated Policies

### Mapping Policy

:::caution Deprecated
The `MappingPolicy` is deprecated. Please see [Rules](./rules.mdx) for how to implement
its behavior using the [Rule Policy](#rule-policy). If you previously used
the `MappingPolicy`, see the
[migration guide](./migration-guide.mdx#manually-migrating-from-the-mapping-policy).
:::

### Fallback Policy

:::caution Deprecated
The `FallbackPolicy` is deprecated. Please see [Fallbacks](./fallback-handoff.mdx#fallbacks) for how to implement
its behavior using the [Rule Policy](#rule-policy). If you previously used
the `FallbackPolicy`, see the
[migration guide](./migration-guide.mdx#manually-migrating-from-the-fallback-policy).
:::

### Two-Stage Fallback Policy

:::caution Deprecated
The `TwoStageFallbackPolicy` is deprecated. Please see [Fallbacks](./fallback-handoff.mdx#fallbacks) for how to implement
its behavior using the [Rule Policy](#rule-policy). If you previously used
the `TwoStageFallbackPolicy`, see the
[migration guide](./migration-guide.mdx#manually-migrating-from-the-two-stage-fallback-policy).
:::

### Form Policy

:::caution Deprecated
The `FormPolicy` is deprecated. Please see [Forms](./forms.mdx) for how to implement
its behavior using the [Rule Policy](#rule-policy). If you previously used
the `FormPolicy`, see the
[migration guide](./migration-guide.mdx#forms).
:::
